Applied LLM Systems RAG, Fine-Tuning & Production Architectures

Applied LLM Systems RAG, Fine-Tuning & Production Architectures

Build it, break it, fix it, ship it - ini 2 days

Most LLM courses stop at architecture diagrams and prompt tips. That’s useless if your job is to deliver working systems under latency, cost, security, and reliability constraints.

This course is designed for engineers who build. Every concept exists only to answer one question: “What do I implement, and why?”

We focus on Python-first LLM system development, covering Retrieval-Augmented Generation, fine-tuning (including LoRA and QLoRA), and hybrid approaches used in real products. You will learn when RAG is the wrong solution, when fine-tuning is wasteful, and how teams actually combine both.

The instructor brings 30+ years of industry experience, teaching patterns that survive production, audits, outages, and scale—not research papers.

Learning Outcomes

By the end of this course, participants will be able to:

• Build end-to-end LLM applications in Python without relying on fragile demos.
• Implement RAG pipelines that are fast, debuggable, and testable.
• Decide when RAG, fine-tuning, LoRA, or QLoRA is the correct solution.
• Build and train fine-tuned models safely and cost-effectively.
• Design context pipelines that don’t collapse under real data.
• Evaluate LLM systems using engineering metrics, not vibes.
• Secure LLM systems against prompt injection and data leakage.
• Deploy and operate LLM systems in production environments.

Prerequisites

If someone struggles with these, they will fall behind.

Participants must have:

• Strong Python engineering skills
  • Classes, dataclasses, typing
  • Async/await and concurrency
  • Error handling and logging
• Real API experience
  • REST
  • JSON schemas
  • Pagination, retries, rate limits
• Solid software engineering fundamentals
  • Data structures
  • Modular design
  • Separation of concerns
• Practical experience with:
  • Git
  • CLI tools
  • Linux/macOS environments
• Comfort reading:
  • SDK source code
  • Technical documentation

Highly recommended

• Docker
• SQL or document databases
• Cloud compute basics
• GPU awareness (not CUDA programming)

Detailed Training Outline

1. LLM Application Engineering: The Reality Check

• Why most LLM projects fail after the demo
• What actually constitutes an “LLM system”
  • Model
  • Prompt
  • Context
  • Data
  • Tools
  • Guardrails
• Deterministic code vs probabilistic components
• Engineering implications of non-determinism
• Design principle: make failure observable

2. Python LLM Client Architecture

• LLM calls as infrastructure, not features
• Sync vs async invocation patterns
• Streaming vs blocking responses
• Token limits as hard constraints
• Retry, timeout, and circuit-breaker strategies
• Centralized client wrappers
• Environment and secrets management

3. Prompting for Engineers (Minimal Theory, Maximum Control)

• Prompts as executable specifications
• Output contracts and schema enforcement
• Prompt composition and reuse
• Defensive prompting
• Prompt versioning and regression control
• Why prompts alone never scale

4. Context Engineering (Where Systems Actually Fail)

• Context pipelines, not prompt strings
• Sources of context
  • Retrieved data
  • Metadata
  • Tool outputs
  • Conversation state
• Context assembly strategies
• Context window budgeting
• Truncation and prioritization logic
• Context contamination and injection risks
• Debugging context failures

5. RAG: Build It Properly or Don’t Use It

• When RAG is the right solution
• When RAG is a terrible idea
• Core RAG architecture (implementation view)
  • Ingestion
  • Chunking
  • Indexing
  • Retrieval
  • Generation
• Stateless vs conversational RAG
• RAG as a data engineering problem

6. Ingestion Pipelines That Don’t Break

• Real-world data sources
  • PDFs
  • HTML
  • Markdown
  • APIs
  • Databases
• Text extraction pitfalls
• Cleaning and normalization
• Chunking strategies
  • Fixed
  • Semantic
  • Structure-aware
• Metadata design for retrieval
• Incremental ingestion and re-indexing

7. Embeddings and Vector Indexes (Engineering View)

• Embedding generation pipelines
• Batch vs streaming embeddings
• Dimensionality tradeoffs
• Similarity metrics and their failure modes
• Vector index design
• Persistence and rebuild strategies
• Multi-tenant isolation

8. Retrieval That Actually Works

• Why top-k similarity is insufficient
• Metadata filtering
• Hybrid retrieval (dense + sparse)
• Query rewriting
• Multi-query retrieval
• Re-ranking pipelines
• Measuring retrieval quality independently of generation

9. Generation Layer Design for RAG

• Prompt templates for grounded answers
• Citation and source traceability
• Refusal strategies
• Multi-document synthesis
• Confidence signaling
• Preventing “creative” answers in factual systems

10. RAG Evaluation and Debugging

• Separating retrieval failures from model failures
• Golden datasets
• Offline vs online evaluation
• Faithfulness vs relevance
• Regression testing across:
  • Prompt changes
  • Model upgrades
  • Index rebuilds
• Observability hooks

11. When RAG Is the Wrong Tool

• Symptoms that indicate RAG misuse
• Latency and cost red flags
• Domain stability vs volatility
• Knowledge vs behavior problems
• The hidden maintenance cost of RAG

12. Fine-Tuning: What It Actually Solves

• What fine-tuning changes (and what it doesn’t)
• Behavior shaping vs knowledge injection
• Dataset construction
• Data quality requirements
• Overfitting risks
• Fine-tuning for:
  • Style
  • Structure
  • Domain behavior
• Why fine-tuning cannot replace retrieval

13. LoRA and QLoRA (Practical, Not Research)

• Why full fine-tuning is often wasteful
• Low-Rank Adaptation (LoRA)
• Quantization-aware fine-tuning (QLoRA)
• GPU, memory, and cost implications
• Training pipelines
• Model versioning and rollback
• Deployment considerations

14. Choosing Between RAG, Fine-Tuning, and Hybrids

• Decision framework
• RAG-only systems
• Fine-tune-only systems
• RAG + fine-tuning hybrids
• Cost, latency, and maintainability tradeoffs
• Organizational constraints that matter more than architecture

15. Security in LLM Systems (Engineer Edition)

• Prompt injection
• Indirect injection via retrieved content
• Data leakage vectors
• Tenant isolation
• Tool abuse
• Logging and auditability
• Fail-closed vs fail-open behavior